Machine learning and rule-based embedding techniques for classifying text documents Article Swipe
Asmaa M. Aubaid
,
Alok Mishra
,
Atul Mishra
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1007/s13198-024-02555-w
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1007/s13198-024-02555-w
Rapid expansion of electronic document archives and the proliferation of online information have made it incredibly difficult to categorize text documents. Classification helps in information retrieval from a conceptual framework. This study addresses the challenge of efficiently categorizing text documents amidst the vast electronic document landscape. Employing machine learning models and a novel document categorization method, W2vRule, we compare its performance with traditional methods. Emphasizing the importance of tuning hyperparameters for optimal performance, the research recommends the W2vRule, a word-to-vector rule-based framework, for improved association-based text classification. The study used the Reuters Newswire dataset. Findings show that W2vRule and machine learning can effectively tell apart important categories. Rule-based approaches perform better than Naive Bayes, BayesNet, Decision Tables, and others in terms of performance metrics.
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- Language
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https://openalex.org/W4403730193Canonical identifier for this work in OpenAlex
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https://doi.org/10.1007/s13198-024-02555-wDigital Object Identifier
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Machine learning and rule-based embedding techniques for classifying text documentsWork title
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articleOpenAlex work type
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enPrimary language
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2024Year of publication
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2024-10-24Full publication date if available
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Asmaa M. Aubaid, Alok Mishra, Atul MishraList of authors in order
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https://doi.org/10.1007/s13198-024-02555-wPublisher landing page
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hybridOpen access status per OpenAlex
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https://doi.org/10.1007/s13198-024-02555-wDirect OA link when available
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Computer science, Artificial intelligence, Embedding, Natural language processing, Machine learning, Information retrievalTop concepts (fields/topics) attached by OpenAlex
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1Total citation count in OpenAlex
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2025: 1Per-year citation counts (last 5 years)
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10Other works algorithmically related by OpenAlex
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| corresponding_author_ids | https://openalex.org/A5100660168 |
| countries_distinct_count | 3 |
| institutions_distinct_count | 3 |
| corresponding_institution_ids | https://openalex.org/I204778367 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/16 |
| sustainable_development_goals[0].score | 0.5899999737739563 |
| sustainable_development_goals[0].display_name | Peace, Justice and strong institutions |
| citation_normalized_percentile.value | 0.72260288 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |